Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images. However, most Network Architecture Search (NAS) approaches in medical images focused on specific datasets and did not take into account the generalization ability of the learned architectures on unseen datasets as well as different domains. In this paper, we address this point by proposing to search for generalizable U-shape architectures on a composited dataset that mixes medical images from multiple segmentation tasks and domains creatively, which is named MixSearch. Specifically, we propose a novel approach to mix multiple small-scale datasets from multiple domains and segmentation tasks to produce a large-scale dataset. Then, a novel weaved encoder-decoder structure is designed to search for a generalized segmentation network in both cell-level and network-level. The network produced by the proposed MixSearch framework achieves state-of-the-art results compared with advanced encoder-decoder networks across various datasets.

翻译：考虑到医疗数据稀缺,医学图像分析中的大多数数据集规模小于自然图像。然而,医疗图像中大多数网络结构搜索(NAS)方法都侧重于特定数据集,没有考虑到在未知数据集和不同领域上所学架构的概括能力。在本文件中,我们通过提议在综合数据集中寻找可通用的U形状结构来解决这一问题,该数据集将多种分层任务和领域创造性地混合在一起,称为MixSearch。具体地说,我们提议采用新颖办法,将多个域的多个小型数据集和分离任务混合起来,以产生大规模数据集。然后,设计了一个新的编织编码解码结构,以在单元格和网络一级搜索一个通用的分解网络。拟议的MixSearch框架产生的网络与各种数据集的高级编码解码网络相比,取得了最新的结果。